Optimization of dry sliding wear characteristics of Al–25Zn/SiC hybrid composites by graphite reinforcement using artificial neural network and Taguchi's method
Restricted accessResearch articleFirst published online March, 2022
Optimization of dry sliding wear characteristics of Al–25Zn/SiC hybrid composites by graphite reinforcement using artificial neural network and Taguchi's method
This paper proposes a unique Al25Zn/SiC/Graphite hybrid composite for plain bearing and other wear-resistant applications. Newly synthesized composites of Al–25Zn alloy reinforced with 3 wt-% graphite and 10, 15 and 20 wt-% SiC are tested using a pin on the disc tribometer following ASTM G-99 standard against EN24 disc. Sliding wear studies using the Taguchi L16 array are carried out for a set of parameters including pin temperature, speed, load and a constant sliding distance. The results reveal that adding 3 wt-% graphite into Al25Zn/SiC composites greatly enhances wear resistance, tensile strength, impact strength and ductility at the expense of hardness. The wear behaviour of the composites is predicted using an artificial neural network and a regression model. Adhesion is found the most common wear mechanism in matrix alloys, while abrasion and delamination in composites. Among the examined materials, the composite with 3 wt-% graphite and 15 wt-% SiC has the optimum combination of characteristics.
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